Electronic device

ABSTRACT

An electronic device comprising a processing unit and a memory that stores a plurality of program instructions. The processing unit executes the program instructions to perform the following steps: (a) storing pixel data of multiple pixels of a picture in the memory, the number of the pixels being greater than the number of pixels in one horizontal line of the picture; (b) performing an integral image operation on the pixel data to obtain integral image data; (c) storing the integral image data in the memory; (d) using the integral image data to calculate a low-frequency component of a target pixel of the picture; and (e) based on the low-frequency component, selectively performing a temporal noise reduction operation on the target pixel.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention generally relates to image processing, and, moreparticularly, to an electronic device that performs noise reduction onimages.

2. Description of Related Art

In image processing, temporal noise reduction (TNR) (also known astemporal filtering) and spatial noise reduction (SNR) (also known asspatial filtering) are often used. TNR uses a previous picture, such asa frame or a field, to perform low-pass filtering on a target picture toreduce noise. Compared with SNR in which the details of the image arelost as the image is often blurred, TNR can maintain the details andtexture of the image. TNR, however, tends to cause ghosting and draggingwhen an object in the image moves; therefore, a motion detectionmechanism is required to prevent ghosting and dragging for imagescontaining moving objects. In addition, since TNR requires the use ofprevious image data, a large amount of memory is required. Because thehardware implementation of TNR is so expensive that the storage ofprevious image data is greatly limited, and the image quality issometimes even sacrificed to save huge hardware costs by usingcompression or downsample techniques, which causes poor user experience.

SUMMARY OF THE INVENTION

In view of the issues of the prior art, an object of the presentinvention is to provide an electronic device and a software-based imageprocessing method to reduce the amount of computation as the processingunit of the electronic device performs image processing, so as to makean improvement to the prior art.

An electronic device is provided. The electronic device includes amemory and a processing unit. The memory is configured to store aplurality of program instructions. The processing unit is coupled to thememory and configured to execute the program instructions to completefollowing steps while executing a driver: (a) receiving pixel data of aplurality of pixels of a picture from an image capture device through auniversal serial bus; (b) storing the pixel data of the pixels in thememory, the number of the pixels being greater than the number of pixelsin one horizontal line of the picture; (c) determining a first regionand a second region in the picture; (d) determining whether a targetpixel is in the first region or the second region; (e) performing steps(e1) to (e4) when the target pixel is in the first region; and (f)performing the temporal noise reduction (TNR) operation on the targetpixel when the target pixel is in the second region. Step (e1) to (e4)are as follows: (e1) performing an integral image operation on the pixeldata to obtain an integral image data; (e2) storing the integral imagedata in the memory; (e3) using the integral image data to calculate alow-frequency component of the target pixel of the picture; and (e4)based on the low-frequency component, selectively performing a temporalnoise reduction operation on the target pixel.

An electronic device is also provided. The electronic device includes amemory and a processing unit. The memory is configured to store aplurality of program instructions. The processing unit is coupled to thememory and configured to execute the program instructions to completefollowing steps: (a) storing pixel data of a plurality of pixels of apicture in the memory, the number of the pixels being greater than thenumber of pixels in one horizontal line of the picture; (b) performingan integral image operation on the pixel data to obtain an integralimage data; (c) storing the integral image data in the memory; (d) usingthe integral image data to calculate a low-frequency component of atarget pixel of the picture; and (e) based on the low-frequencycomponent, selectively performing a temporal noise reduction operationon the target pixel.

An electronic device is also provided. The electronic device includes amemory and a processing unit. The memory is configured to store aplurality of program instructions. The processing unit is coupled to thememory and configured to execute the program instructions to completefollowing steps: (a) storing pixel data of a plurality of pixels of apicture in the memory, the number of the pixels being greater than thenumber of pixels in one horizontal line of the picture; (b) determininga first region and a second region in the picture, the first region andthe second region not overlapping; (c) determining whether a targetpixel is in the first region or the second region; (d) based on alow-frequency component of the target pixel, selectively performing atemporal noise reduction operation on the target pixel when the targetpixel is in the first region; and (e) performing the temporal noisereduction operation on the target pixel when the target pixel is in thesecond region.

The electronic device and the software-based image processing method ofthe present invention employ integral image to reduce the amount ofcomputation of the processing unit in obtaining the low-frequencycomponent of the image. In addition, the present invention can furtherreduce the amount of computation of the processing unit by dividing thepicture into a region of interest and a region of non-interest andselectively performing TNR operations in the region of interest.Compared with the conventional technology, the electronic device and thesoftware-based image processing method of the present invention have theadvantages of implementation flexibility and low cost, and thesoftware-based image processing method is easy to be ported to differentplatforms and facilitates smooth operation of the electronic device.

These and other objectives of the present invention no doubt becomeobvious to those of ordinary skill in the art after reading thefollowing detailed description of the preferred embodiments withreference to the various figures and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an electronic device with an image capture function.

FIG. 2 illustrates a schematic diagram of separately processing ahigh-frequency component and a low-frequency component of an imageaccording to the present invention.

FIG. 3 illustrates a flow chart of obtaining the high-frequencycomponent and low-frequency component of a target pixel according to thepresent invention.

FIG. 4 illustrates the relationship between the original image thatcontains the original pixel data and the integral image that containsthe integral image data.

FIG. 5 illustrates a schematic diagram of dividing a picture intomultiple regions according to the present invention.

FIG. 6 illustrates a flow chart of an image processing method accordingto an embodiment of the present invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following description is written by referring to terms of thistechnical field. If any term is defined in this specification, such termshould be explained accordingly. In addition, the connection betweenobjects or events in the below-described embodiments can be direct orindirect provided that these embodiments are practicable under suchconnection. Said “indirect” means that an intermediate object or aphysical space exists between the objects, or an intermediate event or atime interval exists between the events.

The disclosure herein includes an electronic device and a software-basedimage processing method. Some or all of the processes of thesoftware-based image processing method may be implemented by softwareand/or firmware. A person having ordinary skill in the art can choosecomponents or steps equivalent to those described in this specificationto carry out the present invention, which means that the scope of thisinvention is not limited to the embodiments in the specification.

Because volatile memories (e.g., dynamic random-access memories, DRAMs)in electronic devices are inexpensive, software-based image processingcan obtain a wider range of pixel data at a relatively low cost. Thegreater the range of pixel data, the more reliable the results of imageprocessing. That is, compared to the hardware-based implementation thatis limited to the memory cost, software-based temporal noise reduction(TNR) has advantages of implementation flexibility and low cost.Software-based image processing, however, tends to cause a computationburden on the central processing unit (CPU) (the greater the range ofpixel data, the greater the CPU burden), and a lower-level (i.e., lowercomputing power) CPU may easily cause decreases in the frame rate orslow down the operating speed of other programs that are running, makingit difficult to port the same image processing mechanism to a platformthat employs a different level of CPU. The image processing methodproposed by the present invention can reduce the computation burden ofthe CPU, making the performance of software-based image processing lesslimited to the CPU's computing power, and also making the electronicdevice operate more smoothly (e.g., reducing the occurrence of imagelag).

FIG. 1 is an electronic device with an image capture function. Theelectronic device 100 includes an image capture module 110, a bus 120, aprocessing unit 130, and a memory 140. The image capture module 110includes an image sensing circuit 112 and an image signal processingcircuit 114. The image sensing circuit 112 is, for example, a chargecoupled device (CCD) or a complementary metal oxide semiconductor (CMOS)configured to sense an image of an object and generate an image signal.The image signal processing circuit 114 corrects and compensates theimage signal to generate the original pixels of the image. The imagecapture module 110 communicates with the processing unit 130 through thebus 120. In one embodiment, the bus 120 is a universal serial bus (USB),which means that the data transmission between the image capture module110 and the processing unit 130 conforms to the USB transmissionspecification. The processing unit 130 stores multiple pixel data of onepicture in the memory 140. The memory 140 also stores codes or programinstructions. The processing unit 130 executes the codes or programinstructions to realize the functions of the electronic device 100 andperform the image processing method of the present invention.

A picture generally includes multiple horizontal lines. The number ofsets of pixel data concurrently stored in the memory 140 is greater thanthe number of pixels in one horizontal line. Taking the RGB color spaceas an example, a set of pixel data of one pixel includes three valuesrepresenting red color (R), green color (G), and blue color (B).Assuming that one picture includes N horizontal lines and eachhorizontal line contains M pixels, the number of sets of pixel datastored in the memory 140 at the same time is greater than M. In oneembodiment, the memory 140 concurrently stores pixel data of all pixelsof an entire picture, that is, the memory 140 stores N×M sets of pixeldata at the same time. In addition to the RGB color space, the presentinvention is also applicable to other color spaces (such as YUV),grayscale, and the RAW format.

TNR can effectively filter out noises to obtain a clear and realisticimage without damaging the image details. However, if a certain regionin the picture has a brightness change, performing TNR on that regionwill cause image distortion of moving ghosting in that region.Therefore, when the brightness in the region changes, the presentinvention stops performing the TNR operation in the region. On the otherhand, in order to avoid the poor visual perception caused by suddenoccurrence of noises as the result of the suspension of the TNRoperation and to maintain the consistency of noise reduction in a regionin which the TNR operation is suspended, the present invention performsthe spatial noise reduction (SNR) operation in that region.

FIG. 2 is a schematic diagram of separately processing a high-frequencycomponent and a low-frequency component of an image according to thepresent invention. In order to prevent the TNR effect from beingweakened by noise-triggered misjudgments, after obtaining the originalimage (step S210), the present invention separately processes thehigh-frequency component and the low-frequency component of the originalimage (steps S220 and S230). The low-frequency component corresponds tothe image brightness, and the high-frequency component corresponds tothe image edges and noises. There are two typical reasons for the imagebrightness change: object movement and light change. The movement of anobject includes both a low-frequency change (brightness change caused bythe movement) (step S270) and a high-frequency change (movement of edgesof an object) (step S240), and the light change is mainly alow-frequency change (brightness change) (step S270). Then, the movementof object edge is distinguished from noise according to the magnitude ofthe high-frequency change (steps S250 and S260). The magnitude of thehigh-frequency change due to the movement of object edge is usuallygreater than the magnitude of the high-frequency change due to noise.

FIG. 3 is a flow chart of obtaining the high-frequency component andlow-frequency component of a target pixel according to the presentinvention. First, the processing unit 130 reads the original pixel dataof the picture from the memory 140 (step S310), and then the processingunit 130 calculates an integral image of the picture based on theoriginal pixel data (step S320). FIG. 4 shows the relationship betweenthe original image that contains the original pixel data and theintegral image that contains the integral image data. The calculation ofthe integral image based on the original image is well-known to a personhaving ordinary skill in the art and omitted for brevity. Aftercalculating the integral image of the picture, the processing unit 130stores the integral image in the memory 140 (step S330). Next, theprocessing unit 130 calculates the low-frequency component and thehigh-frequency component of the target pixel according to the integralimage data and a predetermined window size (steps S340 and S350). Morespecifically, assuming that the target pixel is in the center of theimage (represented by slanted lines) and the window size is 3×3(corresponding to the thick black box in FIG. 4), when the processingunit 130 calculates the low-frequency component of the target pixelaccording to the integral image data, the calculation can be exemplifiedin equation (1).

(61−17−6+2)/9=4.44≈4  (1)

Equation (1) is an example of an average low-pass filter that containsthree addition/subtraction operations and one divide operation. Incomparison, when the average low-pass filter operation is performed onthe original image to calculate the low-frequency component of thetarget pixel, the calculation is exemplified in Equation (2).

(5+9+5+7+4+7+2+0+1)/9=4.44 4  (2)

Equation (2) contains eight addition operations and one divideoperation. Obviously, using the integral image for average low-passfiltering the image (i.e., obtaining the low-frequency component) cangreatly reduce the computational complexity of the processing unit 130.This advantage becomes more obvious when the predetermined window isgreater (i.e., more reliable low-frequency component can be obtained).After obtaining the low-frequency component, the processing unit 130subtracts the low-frequency component from the original pixel data ofthe target pixel to obtain the high-frequency component of the targetpixel (step S350).

After obtaining the high-frequency component and low-frequency componentof the target pixel, the processing unit 130 may then determine, basedon FIG. 2, whether object movement, light change, movement of objectedge, or noise is present in the region in which the target pixel islocated. The change in the high-frequency component (or low-frequencycomponent) can be determined by calculating the degree of similarity ofthe high-frequency component (or low-frequency component) between thecurrent picture and the previous picture (e.g., calculating the absolutedifference between the two).

Because spatial filtering is essentially average low-pass filtering, inone embodiment, the processing unit 130 may directly use thelow-frequency component obtained in step S340 as the outcome of thespatial filtering, which further reduces the amount of computation ofthe processing unit 130. The processing unit 130 also performs temporalfiltering on the target pixel. The operation of temporal filtering iswell-known to a person having ordinary skill in the art and omitted forbrevity.

In order to further reduce the amount of computation of the processingunit 130, the present invention divides the picture into multipleregions, and then employs different filtering mechanisms for differentregions. FIG. 5 is a schematic diagram of dividing a picture intomultiple regions according to the present invention. At least threedifferent kinds of regions are included: a region of interest (marked by“ROI”), a region of non-interest (marked by “Non-ROI”), and a transitionregion between the two. The region of interest and region ofnon-interest do not overlap. In this example, the image capture module110 captures the user's image (e.g., during a video call); in this case,the central region of the picture is the region of interest while thefour corners of the picture are regions of non-interest. The transitionregion may be triangular or in other shapes. Typically, the four cornersof the picture (i.e., the regions of non-interest in this example) tendto contain noises, and the central region of the picture (i.e., theregion of interest in this example) tends to have image changes (e.g.,user moving, gesturing, etc.)

FIG. 6 is a flow chart of an image processing method according to anembodiment of the present invention. First, the processing unit 130reads the pixel data from the memory 140 (step S610), and thendetermines whether the target pixel is in the region of interest, regionof non-interest, or transition region according to the pre-definedregions (step S620). When the target pixel is in the region ofnon-interest, the processing unit 130 performs TNR on the image toeffectively eliminate noise in the region of non-interest (step S630).When the target pixel is in the region of interest, the processing unit130 detects whether light change occurs (step S650) and object movementoccurs (step S660) in the location of the target pixel. As describedabove, light change and object movement are highly associated withchanges in the high-frequency component and low-frequency component ofthe image; thus, the flow of FIG. 3 can be utilized to obtain thelow-frequency component and high-frequency component of the target pixelfor further analysis. Ways to obtain the low-frequency component andhigh-frequency component of the target pixel are not limited to the flowof FIG. 3. As mentioned above, object movement includes bothlow-frequency and high-frequency changes whereas light change isprimarily low-frequency change. That is, when the change in thelow-frequency component is greater than a first threshold and the changein the high-frequency component is not greater than a second threshold,an occurrence of light change in the image is implicated (step S650being positive); on the other hand, when the change in the low-frequencycomponent is greater than the first threshold value and the change inthe high-frequency component is greater than the second threshold value,an occurrence of object movement in the image is implicated (step S660being positive); in other cases, there is no light change and no objectmovement in the image.

When any of light change and object movement occurs (i.e., the imagebrightness changes), that is, when the low-frequency component of thetarget pixel changes (i.e., any of step S650 and step S660 is positive),the processing unit 130 performs SNR on the target pixel (step S670).When light change or object movement occurs, performing temporalfiltering on the image may probably cause image distortions such asghosting and dragging. Therefore, spatial filtering is instead performedon the image to reduce noise, so as to avoid ghosting and dragging. Inone embodiment, if the flow of FIG. 3 has already been executed, stepS670 may directly use the low-frequency component of the target pixel(obtained in step S340) as the filtering result of spatial filtering,which reduces the amount of computation of the processing unit 130. Inone embodiment, the processing unit 130 may selectively calculate aweighted average of the spatial filtering result (P_(SNR)) and thetarget pixel's original pixel value (P_(IN)) according to Equation (3)(0≤α≤1) to obtain a final filtering result (P_(out)).

P _(out) =α·P _(SNR)+(1−α)·P _(IN)  (3)

The intensity of SNR can be controlled by adjusting the weighted averagecoefficient α. The greater the a (i.e., the stronger the intensity ofSNR), the more blurred the image. In the case where the TNR operation isnot performed due to the brightness change, when the image is severelyaffected by noise, the SNR operation is usually enhanced to keep theconsistency of noise reduction of the picture with α being set close toor equal to 1 (i.e., P_(out)=P_(SNR)); when the image is less affectedby noise, α can be lowered to prevent the brightness changing regionfrom becoming too blurred due to SNR. Referring to FIG. 6, if neitherlight change nor object movement occurs in the location of the targetpixel, the processing unit 130 performs TNR on the target pixel toeffectively reduce noise. (Step S680). In brief, according to thelow-frequency component of the target pixel, steps S650 and S660determine to perform one of the following operations on the targetpixel: (1) pure SNR (one embodiment of step S670); (2) pure TNR (stepS680); or (3) SNR as well as calculation of weighted average of the SNRresult and the original pixel value (another embodiment of step S670).After completing steps S670 and S680, the processing unit 130 outputsthe filtering result (step S690).

In the case where the target pixel is in the transition region, theprocessing unit 130 performs SNR and/or TNR on the target pixel (stepS640). In one embodiment, the processing unit 130 can directly selectone of SNR and TNR to filter the target pixel. In another embodiment, inorder to avoid discontinuousness of the image, the processing unit 130performs both SNR and TNR on the target pixel and mixes the results ofthe two operations to obtain a final filtering result. For example, theprocessing unit 130 may calculate the weighted average of the twooperation results according to Equation (4):

P _(out) =β·P _(TNR)(1−β)·P _(SNR)  (4)

P_(out) is the final filtering result, P_(TNR) is the result of temporalfiltering, P_(SNR) is the result of spatial filtering, and β is theweighted average coefficient (0≤β≤1). When the position of the targetpixel is closer to the region of non-interest, β can be set closer to 1.Similarly, after step S640 is completed, the processing unit 130 outputsthe filtering result (step S690). There is not limitation on the size ofthe window used in the above steps S630, S640, S670 and S680, and thewindow size used in each step may be the same or different.

In one embodiment, the software-based image processing method describedabove is completed by the processing unit 130 while executing a driverof the electronic device 100. Completing the method during the executionof the driver means that when executing the image processing method ofthe present invention, the processing unit 130 can directly obtain thepixel data from the image capture module 110 (transmitted through thebus 120) and then store the pixel data in the memory 140. In comparison,in the case where the image processing method of the present inventionis completed by the processing unit 130 while executing an applicationof the electronic device 100, the processing unit 130 cannot directlyobtain the image data from the image capture module 110, but reads thepixel data that have been stored in the memory 140 and have beenprocessed by the driver and/or other applications.

Please note that there is no step sequence limitation for the methodinventions as long as the execution of each step is applicable.Furthermore, the shape, size, and ratio of any element and the stepsequence of any flow chart in the disclosed figures are exemplary forunderstanding, not for limiting the scope of this invention. Theaforementioned descriptions represent merely the preferred embodimentsof the present invention, without any intention to limit the scope ofthe present invention thereto. Various equivalent changes, alterations,or modifications based on the claims of the present invention are allconsequently viewed as being embraced by the scope of the presentinvention.

What is claimed is:
 1. An electronic device comprising: a memoryconfigured to store a plurality of program instructions; a processingunit coupled to the memory and configured to execute the programinstructions to complete following steps while executing a driver: (a)receiving pixel data of a plurality of pixels of a picture from an imagecapture device through a universal serial bus; (b) storing the pixeldata of the pixels in the memory, the number of the pixels being greaterthan the number of pixels in one horizontal line of the picture; (c)determining a first region and a second region in the picture; (d)determining whether a target pixel is in the first region or the secondregion; (e) performing following steps when the target pixel is in thefirst region: (e1) performing an integral image operation on the pixeldata to obtain an integral image data; (e2) storing the integral imagedata in the memory; (e3) using the integral image data to calculate alow-frequency component of the target pixel of the picture; and (e4)based on the low-frequency component, selectively performing a temporalnoise reduction operation on the target pixel; and (f) performing thetemporal noise reduction operation on the target pixel when the targetpixel is in the second region.
 2. The electronic device of claim 1,wherein the first region and the second region do not overlap, and theprocessing unit further executes the program instructions to completefollowing steps: (g) determining a third region in the picture, thethird region being different from the first region and the secondregion; and (h) performing the temporal noise reduction operation and/ora spatial noise reduction operation on the target pixel when the targetpixel is in the third region.
 3. The electronic device of claim 1,wherein step (e4) comprises: determining a magnitude of brightnesschange of the picture according to the low-frequency component, and,based on the magnitude of brightness change, selectively performing thetemporal noise reduction operation on the target pixel.
 4. Theelectronic device of claim 1, wherein step (e) further comprises: (e5)performing a spatial noise reduction operation on the target pixel whenstep (e4) does not perform the temporal noise reduction operation. 5.The electronic device of claim 4, wherein step (e) further comprises:(e6) calculating a weighted average of a result of the spatial noisereduction operation and the pixel data of the target pixel.
 6. Anelectronic device comprising: a memory configured to store a pluralityof program instructions; a processing unit coupled to the memory andconfigured to execute the program instructions to complete followingsteps: (a) storing pixel data of a plurality of pixels of a picture inthe memory, the number of the pixels being greater than the number ofpixels in one horizontal line of the picture; (b) performing an integralimage operation on the pixel data to obtain an integral image data; (c)storing the integral image data in the memory; (d) using the integralimage data to calculate a low-frequency component of a target pixel ofthe picture; and (e) based on the low-frequency component, selectivelyperforming a temporal noise reduction operation on the target pixel. 7.The electronic device of claim 6, wherein the processing unit furtherexecutes the program instructions to complete following steps beforestep (b): (f) determining a first region and a second region in thepicture, the first region and the second region not overlapping; (g)determining whether the target pixel is in the first region or thesecond region; (h) performing step (b) to step (e) when the target pixelis in the first region; and (i) directly performing the temporal noisereduction operation on the target pixel without performing step (b) tostep (e) when the target pixel is in the second region.
 8. Theelectronic device of claim 7, wherein the processing unit furtherexecutes the program instructions to complete following steps: (j)determining a third region in the picture, the third region beingdifferent from the first region and the second region; and (k)performing the temporal noise reduction operation and/or a spatial noisereduction operation on the target pixel when the target pixel is in thethird region.
 9. The electronic device of claim 6, wherein step (e)comprises: (e1) determining a magnitude of brightness change of thepicture according to the low-frequency component, and, based on themagnitude of brightness change, selectively performing the temporalnoise reduction operation on the target pixel.
 10. The electronic deviceof claim 6, wherein the processing unit further executes the programinstructions to complete following steps: (f) performing a spatial noisereduction operation on the target pixel when step (e) does not performthe temporal noise reduction operation.
 11. The electronic device ofclaim 10, wherein step (f) comprises: (f1) using the low-frequencycomponent as an outcome of the spatial noise reduction operation. 12.The electronic device of claim 10, wherein the processing unit furtherexecutes the program instructions to complete following steps: (g)calculating a weighted average of a result of the spatial noisereduction operation and the pixel data of the target pixel.
 13. Theelectronic device of claim 6, wherein the electronic device furthercomprises an image capture device, the image capture device and theprocessing unit transmit data through a universal serial bus, and theelectronic device executes the program instructions to complete theforegoing steps and following steps while executing a driver: (f)receiving the pixel data of the pixels from the image capture devicethrough the universal serial bus before the pixel data of the pixels areconcurrently stored in the memory.
 14. An electronic device comprising:a memory configured to store a plurality of program instructions; aprocessing unit coupled to the memory and configured to execute theprogram instructions to complete following steps: (a) storing pixel dataof a plurality of pixels of a picture in the memory, the number of thepixels being greater than the number of pixels in one horizontal line ofthe picture; (b) determining a first region and a second region in thepicture, the first region and the second region not overlapping; (c)determining whether a target pixel is in the first region or the secondregion; (d) based on a low-frequency component of the target pixel,selectively performing a temporal noise reduction operation on thetarget pixel when the target pixel is in the first region; and (e)performing the temporal noise reduction operation on the target pixelwhen the target pixel is in the second region.
 15. The electronic deviceof claim 14, wherein the processing unit further executes the programinstructions to complete following steps: (f) determining a third regionin the picture, the third region being different from the first regionand the second region; and (g) performing the temporal noise reductionoperation and/or a spatial noise reduction operation on the target pixelwhen the target pixel is in the third region.
 16. The electronic deviceof claim 14, wherein step (d) comprises: (d1) determining a magnitude ofbrightness change of the picture according to the low-frequencycomponent, and, based on the magnitude of brightness change, selectivelyperforming the temporal noise reduction operation on the target pixel.17. The electronic device of claim 14, wherein the processing unitfurther executes the program instructions to complete following steps:(f) performing a spatial noise reduction operation on the target pixelwhen step (d) does not perform the temporal noise reduction operation.18. The electronic device of claim 17, wherein step (f) comprises: (f1)using the low-frequency component as an outcome of the spatial noisereduction operation.
 19. The electronic device of claim 17, wherein theprocessing unit further executes the program instructions to completefollowing steps: (g) calculating a weighted average of a result of thespatial noise reduction operation and the pixel data of the targetpixel.
 20. The electronic device of claim 14, wherein the electronicdevice further comprises an image capture device, the image capturedevice and the processing unit transmit data through a universal serialbus, and the electronic device executes the program instructions tocomplete the foregoing steps and following steps while executing adriver: (f) receiving the pixel data of the pixels from the imagecapture device through the universal serial bus before the pixel data ofthe pixels are concurrently stored in the memory.